Operational Wave Prediction System at Environment Canada: Going Global to Improve Regional Forecast Skill

Abstract A global deterministic wave prediction system (GDWPS) is used to improve regional forecasts of waves off the Canadian coastline and help support the practice of safe marine activities in Canadian waters. The wave model has a grid spacing of ¼° with spectral resolution of 36 frequency bins and 36 directional bins. The wave model is driven with hourly 10-m winds generated by the operational global atmospheric prediction system. Ice conditions are updated every three hours using the ice concentration forecasts generated by the Global Ice–Ocean Prediction System. Wave forecasts are evaluated over two periods from 15 August to 31 October 2014 and from 15 December 2014 to 28 February 2015, as well as over select cases during the fall of 2012. The global system is shown to improve wave forecast skill over regions where forecasts were previously produced using limited-area models only. The usefulness of a global expansion is demonstrated for large swell events affecting the northeast Pacific. The first validation of a Canadian operational wave forecast system in the Arctic is presented. Improvements in the representation of forecast wave fields associated with tropical cyclones are also demonstrated. Finally, the GDWPS is shown to result in gains of at least 12 h of lead time.

Download Full-text

Forecast skill score assessment of a relocatable ocean prediction system, using a simplified objective analysis method

Ocean Science ◽

10.5194/os-13-925-2017 ◽

2017 ◽

Vol 13 (6) ◽

pp. 925-945 ◽

Cited By ~ 3

Author(s):

Reiner Onken

Keyword(s):

Observational Data ◽

Skill Score ◽

Forecast Skill ◽

Objective Analysis ◽

Prediction System ◽

Regional Ocean Modeling System ◽

Data Set ◽

Vast Number ◽

Ocean Prediction ◽

Forecast Skill Score

Abstract. A relocatable ocean prediction system (ROPS) was employed to an observational data set which was collected in June 2014 in the waters to the west of Sardinia (western Mediterranean) in the framework of the REP14-MED experiment. The observational data, comprising more than 6000 temperature and salinity profiles from a fleet of underwater gliders and shipborne probes, were assimilated in the Regional Ocean Modeling System (ROMS), which is the heart of ROPS, and verified against independent observations from ScanFish tows by means of the forecast skill score as defined by Murphy(1993). A simplified objective analysis (OA) method was utilised for assimilation, taking account of only those profiles which were located within a predetermined time window W. As a result of a sensitivity study, the highest skill score was obtained for a correlation length scale C = 12.5 km, W = 24 h, and r = 1, where r is the ratio between the error of the observations and the background error, both for temperature and salinity. Additional ROPS runs showed that (i) the skill score of assimilation runs was mostly higher than the score of a control run without assimilation, (i) the skill score increased with increasing forecast range, and (iii) the skill score for temperature was higher than the score for salinity in the majority of cases. Further on, it is demonstrated that the vast number of observations can be managed by the applied OA method without data reduction, enabling timely operational forecasts even on a commercially available personal computer or a laptop.

Download Full-text

A Regional Ensemble Prediction System Based on Moist Targeted Singular Vectors and Stochastic Parameter Perturbations

Monthly Weather Review ◽

10.1175/2007mwr2109.1 ◽

2008 ◽

Vol 136 (2) ◽

pp. 443-462 ◽

Cited By ~ 41

Author(s):

Xiaoli Li ◽

Martin Charron ◽

Lubos Spacek ◽

Guillem Candille

Keyword(s):

Deep Convection ◽

Horizontal Resolution ◽

Forecast Skill ◽

Convective Precipitation ◽

Ensemble Prediction ◽

Limited Area ◽

Prediction System ◽

Ensemble Prediction System ◽

Stochastic Perturbations ◽

Singular Vectors

Abstract A regional ensemble prediction system (REPS) with the limited-area version of the Canadian Global Environmental Multiscale (GEM) model at 15-km horizontal resolution is developed and tested. The total energy norm singular vectors (SVs) targeted over northeastern North America are used for initial and boundary perturbations. Two SV perturbation strategies are tested: dry SVs with dry simplified physics and moist SVs with simplified physics, including stratiform condensation and convective precipitation as well as dry processes. Model physics uncertainties are partly accounted for by stochastically perturbing two parameters: the threshold vertical velocity in the trigger function of the Kain–Fritsch deep convection scheme, and the threshold humidity in the Sundqvist explicit scheme. The perturbations are obtained from first-order Markov processes. Short-range ensemble forecasts in summer with 16 members are performed for five different experiments. The experiments employ different perturbation and piloting strategies, and two different surface schemes. Verification focuses on quantitative precipitation forecasts and is done using a range of probabilistic measures. Results indicate that using moist SVs instead of dry SVs has a stronger impact on precipitation than on dynamical fields. Forecast skill for precipitation is greatly influenced by the dominant synoptic weather systems. For stratiform precipitation caused by strong baroclinic systems, the forecast skill is improved in the moist SV experiments relative to the dry SV experiments. For convective precipitation rates in the range 15–50 mm (24 h)−1 produced by weak synoptic baroclinic systems, all experiments exhibit noticeably poorer forecast skills. Skill improvements due to the Interactions between Soil, Biosphere, and Atmosphere (ISBA) surface scheme and stochastic perturbations are also observed.

Download Full-text

Referee Comments of the manuscript 'Forecast skill score assessment of a relocatable ocean prediction system, using a simplified objective analysis method'

10.5194/os-2017-35-rc1 ◽

2017 ◽

Author(s):

Anonymous

Keyword(s):

Skill Score ◽

Forecast Skill ◽

Objective Analysis ◽

Prediction System ◽

Analysis Method ◽

Ocean Prediction ◽

Forecast Skill Score

Download Full-text

Forecast skill score assessment of a relocatable ocean prediction system, using a simplified objective analysis method

10.5194/os-2017-35 ◽

2017 ◽

Author(s):

Reiner Onken

Keyword(s):

Observational Data ◽

Skill Score ◽

Forecast Skill ◽

Objective Analysis ◽

Prediction System ◽

Regional Ocean Modeling System ◽

Data Set ◽

Vast Number ◽

Ocean Prediction ◽

Forecast Skill Score

Abstract. A Relocatable Ocean Prediction System (ROPS) was employed to an observational data set which was collected in June 2014 in the waters to the west of Sardinia (Western Mediterranean) in the mainframe of the REP14-MED experiment. The observational data, comprising almost 5000 temperature and salinity profiles from a fleet of underwater gliders and shipborne probes, were assimilated in the Regional Ocean Modeling System (ROMS) which is the heart of ROPS, and verified against independent observations from ScanFish tows by means of the forecast skill score as defined by Murphy (1993). A simplified objective analysis (OA) method was utilised for assimilation, taking account of only those profiles which were located within a predetermined time window W. As a result of a sensitivity study, the highest skill score was obtained for a correlation length scale C = 12.5 km, W = 24 hours, and r = 1, where r is the ratio between the error of the observations and the background error, both for temperature and salinity. Additional ROPS runs showed that (i) the skill score of assimilation runs was mostly higher than the score of a control run without assimilation, (i) the skill score increased with increasing forecast range, and (iii) the skill score for temperature was higher than the score for salinity in the majority of cases. Further on, it is demonstrated that the vast number of observations can be managed by the applied OA method without data reduction, enabling timely operational forecasts even on a commercially available Personal Computer or a laptop.

Download Full-text

Review of the paper: ‘Forecast skill score assessment of a relocatable ocean prediction system, using a simplified objective analysis method’, by Reiner Onken.

10.5194/os-2017-35-rc3 ◽

2017 ◽

Author(s):

Anonymous

Keyword(s):

Skill Score ◽

Forecast Skill ◽

Objective Analysis ◽

Prediction System ◽

Analysis Method ◽

Ocean Prediction ◽

Forecast Skill Score

Download Full-text

Development and application of NMEFC Arctic Ice-Ocean Prediction System (ArcIOPS) of CHINA

10.5194/egusphere-egu2020-2497 ◽

2020 ◽

Author(s):

Xi Liang ◽

Fu Zhao ◽

Chunhua Li ◽

Lin Zhang

Keyword(s):

Sea Ice ◽

Early Stage ◽

The Arctic ◽

Ice Thickness ◽

Prediction System ◽

Sea Ice Concentration ◽

Sea Ice Thickness ◽

Ice Concentration ◽

Ocean Prediction ◽

Arctic Ice

<p>NMEFC provides sea ice services for the CHINARE since 2010, the products in the early stage (before 2017) include satellite-retrieved and numerical forecasts of sea ice concentration. Based on MITgcm and ensemble Kalman Filter data assimilation scheme, &#160;the Arctic Ice-Ocean Prediction System (ArcIOPS v1.0), was established in 2017. ArcIOPS v1.0 assimilates available satellite-retrieved sea ice concentration and thickness data. Sea ice thickness forecasting products from ArcIOPS v1.0 are provided to the CHINARE8, and are believed to have played an important role in the successful passage of R/V XUELONG through the Central Arctic for the first time during the summer of 2017. In 2019, ArcIOPS v1.0 was upgraded to the latest version (ArcIOPS v1.1), which assimilates satellite-retrieved sea ice concentration, sea ice thickness, as well as sea surface temperature (SST) data in ice free areas. Comparison between outputs of the latest version of ArcIOPS and that of its previous version shows that the latest version has a substantial improvement on sea ice concentration forecasts.&#160;In the future, with more and more kinds of observations to be assimilated, the high-resolution version of ArcIOPS will be put into operational running and benefit Chinese scientific and commercial activities in the Arctic Ocean.</p>

Download Full-text

Prévoir les variations saisonnières de la glace de mer arctique et leurs impacts sur le climat

La Météorologie ◽

10.37053/lameteorologie-2020-0089 ◽

2020 ◽

pp. 024

Author(s):

Rym Msadek ◽

Gilles Garric ◽

Sara Fleury ◽

Florent Garnier ◽

Lauriane Batté ◽

...

Keyword(s):

Sea Ice ◽

Arctic Region ◽

Arctic Sea Ice ◽

Climate Impacts ◽

The Arctic ◽

Recent Effort ◽

Sea Ice Thickness ◽

Arctic Sea ◽

Ice Conditions ◽

Upper Level

L'Arctique est la région du globe qui s'est réchauffée le plus vite au cours des trente dernières années, avec une augmentation de la température de surface environ deux fois plus rapide que pour la moyenne globale. Le déclin de la banquise arctique observé depuis le début de l'ère satellitaire et attribué principalement à l'augmentation de la concentration des gaz à effet de serre aurait joué un rôle important dans cette amplification des températures au pôle. Cette fonte importante des glaces arctiques, qui devrait s'accélérer dans les décennies à venir, pourrait modifier les vents en haute altitude et potentiellement avoir un impact sur le climat des moyennes latitudes. L'étendue de la banquise arctique varie considérablement d'une saison à l'autre, d'une année à l'autre, d'une décennie à l'autre. Améliorer notre capacité à prévoir ces variations nécessite de comprendre, observer et modéliser les interactions entre la banquise et les autres composantes du système Terre, telles que l'océan, l'atmosphère ou la biosphère, à différentes échelles de temps. La réalisation de prévisions saisonnières de la banquise arctique est très récente comparée aux prévisions du temps ou aux prévisions saisonnières de paramètres météorologiques (température, précipitation). Les résultats ayant émergé au cours des dix dernières années mettent en évidence l'importance des observations de l'épaisseur de la glace de mer pour prévoir l'évolution de la banquise estivale plusieurs mois à l'avance. Surface temperatures over the Arctic region have been increasing twice as fast as global mean temperatures, a phenomenon known as arctic amplification. One main contributor to this polar warming is the large decline of Arctic sea ice observed since the beginning of satellite observations, which has been attributed to the increase of greenhouse gases. The acceleration of Arctic sea ice loss that is projected for the coming decades could modify the upper level atmospheric circulation yielding climate impacts up to the mid-latitudes. There is considerable variability in the spatial extent of ice cover on seasonal, interannual and decadal time scales. Better understanding, observing and modelling the interactions between sea ice and the other components of the climate system is key for improved predictions of Arctic sea ice in the future. Running operational-like seasonal predictions of Arctic sea ice is a quite recent effort compared to weather predictions or seasonal predictions of atmospheric fields like temperature or precipitation. Recent results stress the importance of sea ice thickness observations to improve seasonal predictions of Arctic sea ice conditions during summer.

Download Full-text